A Deep Learning model to segment liver metastases on CT images acquired at different time-points during chemotherapy

The aim of this study is to present a fully automatic deep learning algorithm to segment liver Colorectal cancer metastases (lmCRC) on CT images, based on a U-Net structure, comparing nets with and without the transfer learning approach. This is a bi-centric study, enrolling patients who underwent CT exam before (baseline) and after first-line therapy (TP1). Patients were divided into training (using a portion of baseline sequences from both centers) to train the DL model, and two validation sets: one with baseline (valB), and one with TP1 (valTP1) sequences. The reference standard for the automatic segmentations was defined by the manual segmentations performed by an experienced radiologist on the portal phase of the baseline and TP1 CT exam. The best performing model obtained Dice Similarity Coefficient (DSC) of 0.68±0.24, Precision (Pr) of 0.74±0.27, Recall (Re) of 0.73±0.26, Detection Rate (DR) of 93% on the valB, and DSC of 0.61±0.28, Pr of 0.68±0.31, Re of 0.65±0.29 and DR of 88% on the valTP1. These encouraging results, if confirmed on larger dataset, might provide a reliable and robust tool that can be used as first step of future radiomics analyses aimed at predicting response to therapy, improving the management of lmCRC patients

Cure indicators and prevalence by stage at diagnosis for breast and colorectal cancer patients: A population‐based study in Italy

People alive many years after breast (BC) or colorectal cancer (CRC) diagnoses are increasing. This paper aimed to estimate the indicators of cancer cure and complete prevalence for Italian patients with BC and CRC by stage and age. A total of 31 Italian Cancer Registries (47% of the population) data until 2017 were included. Mixture cure models allowed estimation of net survival (NS); cure fraction (CF); time to cure (TTC, 5-year conditional NS >95%); cure prevalence (who will not die of cancer); and already cured (prevalent patients living longer than TTC). 2.6% of all Italian women (806,410) were alive in 2018 after BC and 88% will not die of BC. For those diagnosed in 2010, CF was 73%, 99% when diagnosed at stage I, 81% at stage II, and 36% at stages III-IV. For all stages combined, TTC was >10 years under 45 and over 65 years and for women with advanced stages, but <= 1 year for all BC patients at stage I. The proportion of already cured prevalent BC women was 75% (94% at stage I). Prevalent CRC cases were 422,407 (0.7% of the Italian population), 90% will not die of CRC. For CRC patients, CF was 56%, 92% at stage I, 71% at stage II, and 35% at stages III-IV. TTC was <= 10 years for all age groups and stages. Already cured were 59% of all prevalent CRC patients (93% at stage I). Cancer cure indicators by stage may contribute to appropriate follow-up in the years after diagnosis, thus avoiding patients' discrimination

The Economic Impact of Rectal Cancer: A Population-Based Study in Italy

Costs of cancer care are increasing worldwide, and sustainability of cancer burden is critical. In this study, the economic impact of rectal cancer on the Italian healthcare system, measured as public healthcare expenditure related to investigation and treatment of rectal cancer patients is estimated. A cross-sectional cohort of 9358 rectal cancer patients is linked, on an individual basis, to claims associated to rectal cancer diagnosis and treatments. Costs refer mainly to years 2010–2011 and are estimated by phase of care, as healthcare needs vary along the care pathway: diagnostic procedures are mainly provided in the first year, surveillance procedures are addressed to chronically ill patients, and end-of-life procedures are given in the terminal status. Clinical approaches and corresponding costs are specific by cancer type and vary by phase of care, stage at diagnosis, and age. Surgery is undertaken by the great majority of patients. Thus, hospitalization is the main cost driver. The evidence produced can be used to improve planning and allocation of healthcare resources. In particular, early diagnosis of rectal cancer is a gain in healthcare budget. Policies raising spreading of and adherence to screening plans, above all when addressed to people living in Southern Italy, should be strongly encouraged

Patterns of care and cost profiles of women with breast cancer in Italy: EPICOST study based on real world data

Objectives To estimate total direct health care costs associated to diagnosis and treatment of women with breast cancer in Italy, and to investigate their distribution by service type according to the disease pathway and patient characteristics. Methods Data on patients provided by population-based Cancer Registries are linked at individual level with data on health-care services and corresponding claims from administrative databases. A combination of cross-sectional approach and a threephase of care decomposition model with initial, continuing and final phases-of-care defined according to time occurred since diagnosis and disease outcome is adopted. Direct estimation of cancer-related costs is obtained. Results Study cohort included 49,272 patients, 15.2% were in the initial phase absorbing 42% of resources, 79.7% in the continuing phase absorbing 44% of resources and 5.1% in the final phase absorbing 14% of resources. Hospitalization was the most important cost driver, accounting for over 55% of the total costs. Conclusions This paper represents the first attempt in Italy to estimate the economic burden of cancer at population level taking into account the entire disease pathway and using multiple current health care databases. The evidence produced by the study can be used to better plan resources allocation. The model proposed is replicable to countries with individual health care information on services and claims

Adolescent and Young Adult Cancer Survivors: Design and Characteristics of the First Nationwide Population-Based Cohort in Italy

Purpose: Adolescent and young adult (AYA, 15-39 years) cancer survivors (alive at least 5 years after cancer diagnosis) are less studied than younger and older cancer survivors and research on their late effects is limited. To facilitate research on long-term outcomes of AYA cancer survivors, we established, in Italy, a population-based AYA cancer survivors' cohort. This article describes the study design and main characteristics of this cohort.Methods: The cohort derives from population-based cancer registries (CRs). Each CR identified AYA cancer patients retrospectively. Treatment for first primary cancer and all health events from diagnosis to death can be traced through linkage with available health databases, such as hospital discharge records (HDRs), mortality files, and outpatient and pharmaceutical databases.Results: Thirty-four CRs participated to the cohort which overall includes 93,291 AYAs with cancer and 67,692 cancer survivors. First primary cancer distribution in AYA cancer survivors differs by sex and age groups because of the different cancer types diagnosed in AYAs. Almost 78% of AYA cancer survivors have HDRs and 14.8% also pharmaceutical and outpatient databases.Conclusion: This cohort will be used to study, for the first time in Italy, the pattern and excess risk of late effects in AYA cancer survivors. HDRs, outpatient and pharmaceutical databases will be used to define primary treatment to assess its impact on AYA cancer survivors' late effects. This cohort exploiting data sources already available at CRs, minimize the data collection effort and it will contribute to assess the feasibility of using administrative database to study cancer survivors' late effects

Complete prevalence and indicators of cancer cure: enhanced methods and validation in Italian population-based cancer registries

ObjectivesTo describe the procedures to derive complete prevalence and several indicators of cancer cure from population-based cancer registries. Materials and methodsCancer registry data (47% of the Italian population) were used to calculate limited duration prevalence for 62 cancer types by sex and registry. The incidence and survival models, needed to calculate the completeness index (R) and complete prevalence, were evaluated by likelihood ratio tests and by visual comparison. A sensitivity analysis was conducted to explore the effect on the complete prevalence of using different R indexes. Mixture cure models were used to estimate net survival (NS); life expectancy of fatal (LEF) cases; cure fraction (CF); time to cure (TTC); cure prevalence, prevalent patients who were not at risk of dying as a result of cancer; and already cured patients, those living longer than TTC at a specific point in time. CF was also compared with long-term NS since, for patients diagnosed after a certain age, CF (representing asymptotical values of NS) is reached far beyond the patient's life expectancy. ResultsFor the most frequent cancer types, the Weibull survival model stratified by sex and age showed a very good fit with observed survival. For men diagnosed with any cancer type at age 65-74 years, CF was 41%, while the NS was 49% until age 100 and 50% until age 90. In women, similar differences emerged for patients with any cancer type or with breast cancer. Among patients alive in 2018 with colorectal cancer at age 55-64 years, 48% were already cured (had reached their specific TTC), while the cure prevalence (lifelong probability to be cured from cancer) was 89%. Cure prevalence became 97.5% (2.5% will die because of their neoplasm) for patients alive >5 years after diagnosis. ConclusionsThis study represents an addition to the current knowledge on the topic providing a detailed description of available indicators of prevalence and cancer cure, highlighting the links among them, and illustrating their interpretation. Indicators may be relevant for patients and clinical practice; they are unambiguously defined, measurable, and reproducible in different countries where population-based cancer registries are active

Complete cancer prevalence in Europe in 2020 by disease duration and country (EUROCARE-6): a population-based study

Background Cancer survivors-people living with and beyond cancer-are a growing population with different health needs depending on prognosis and time since diagnosis. Despite being increasingly necessary, complete information on cancer prevalence is not systematically available in all European countries. We aimed to fill this gap by analysing population-based cancer registry data from the EUROCARE-6 study. Methods In this population-based study, using incidence and follow-up data up to Jan 1, 2013, from 61 cancer registries, complete and limited-duration prevalence by cancer type, sex, and age were estimated for 29 European countries and the 27 countries in the EU (EU27; represented by 22 member states that contributed registry data) using the completeness index method. We focused on 32 malignant cancers defined according to the third edition of the International Classification of Diseases for Oncology, and only the first primary tumour was considered when estimating the prevalence. Prevalence measures are expressed in terms of absolute number of prevalent cases, crude prevalence proportion (reported as percentage or cases per 100 000 resident people), and age-standardised prevalence proportion based on the European Standard Population 2013. We made projections of cancer prevalence proportions up to Jan 1, 2020, using linear regression. Findings In 2020, 23 711 thousand (95% CI 23 565-23 857) people (5 center dot 0% of the population) were estimated to be alive after a cancer diagnosis in Europe, and 22 347 thousand (95% CI 22 210-22 483) in EU27. Cancer survivors were more frequently female (12 818 thousand [95% CI 12 720-12 917]) than male (10 892 thousand [10 785-11 000]). The five leading tumours in female survivors were breast cancer, colorectal cancer, corpus uterine cancer, skin melanoma, and thyroid cancer (crude prevalence proportion from 2270 [95%CI 2248-2292] per 100 000 to 301 [297-305] per 100 000). Prostate cancer, colorectal cancer, urinary bladder cancer, skin melanoma, and kidney cancer were the most common tumours in male survivors (from 1714 [95% CI 1686-1741] per 100 000 to 255 [249-260] per 100 000). The differences in prevalence between countries were large (from 2 to 10 times depending on cancer type), in line with the demographic structure, incidence, and survival patterns. Between 2010 and 2020, the number of prevalent cases increased by 3 center dot 5% per year (41% overall), partly due to an ageing population. In 2020, 14 850 thousand (95% CI 14 681-15 018) people were estimated to be alive more than 5 years after diagnosis and 9099 thousand (8909-9288) people were estimated to be alive more than 10 years after diagnosis, representing an increasing proportion of the cancer survivor population. Interpretation Our findings are useful at the country level in Europe to support evidence-based policies to improve the quality of life, care, and rehabilitation of patients with cancer throughout the disease pathway. Future work includes estimating time to cure by stage at diagnosis in prevalent cases. Copyright (c) 2024 Elsevier Ltd. All rights reserved